class_docstr: An `EventMultiplexer` manages access to multiple `EventAccumulator`s.
  
  Each `EventAccumulator` is associated with a `run`, which is a self-contained
  TensorFlow execution. The `EventMultiplexer` provides methods for extracting
  information about events from multiple `run`s.
  
  Example usage for loading specific runs from files:
  
  ```python
  x = EventMultiplexer({'run1': 'path/to/run1', 'run2': 'path/to/run2'})
  x.Reload()
  ```
  
  Example usage for loading a directory where each subdirectory is a run
  
  ```python
  (eg:) /parent/directory/path/
        /parent/directory/path/run1/
        /parent/directory/path/run1/events.out.tfevents.1001
        /parent/directory/path/run1/events.out.tfevents.1002
  
        /parent/directory/path/run2/
        /parent/directory/path/run2/events.out.tfevents.9232
  
        /parent/directory/path/run3/
        /parent/directory/path/run3/events.out.tfevents.9232
  x = EventMultiplexer().AddRunsFromDirectory('/parent/directory/path')
  (which is equivalent to:)
  x = EventMultiplexer({'run1': '/parent/directory/path/run1', 'run2':...}
  ```
  
  If you would like to watch `/parent/directory/path`, wait for it to be created
    (if necessary) and then periodically pick up new runs, use
    `AutoloadingMultiplexer`
  @@Tensors
docstr: Return all the run names in the `EventMultiplexer`.
  
  Returns:
  ```
    {runName: { scalarValues: [tagA, tagB, tagC],
                graph: true, meta_graph: true}}
  ```
docstr_tok: ["Return","all","the","run","names","in","the","`","EventMultiplexer","`",".","Returns",":","``","`","{","runName",":","{","scalarValues",":","[","tagA",",","tagB",",","tagC","]",",","graph",":","true",",","meta_graph",":","true","}","}","``","`"]
code: def Runs(self):
      """Return all the run names in the `EventMultiplexer`.
  
      Returns:
      ```
        {runName: { scalarValues: [tagA, tagB, tagC],
                    graph: true, meta_graph: true}}
      ```
      """
      with self._accumulators_mutex:
          # To avoid nested locks, we construct a copy of the run-accumulator map
          items = list(self._accumulators.items())
      return {
          run_name: accumulator.Tags()
          for run_name, accumulator in items
      }
  
imports: import os
  import queue
  import threading
  from typing import Optional
  from tensorboard.backend.event_processing import directory_watcher
  from tensorboard.backend.event_processing import plugin_event_accumulator
  from tensorboard.backend.event_processing import io_wrapper
  from tensorboard.util import tb_logging
label_desc: Use image node_id 18 for calling the EventMultiplexer obj's underlying member method code with example usage: obj.Runs() and returns: unknown